Granuloma Annulare Exhibits Mixed Immune and Macrophage Polarization Profiles with Spatial Transcriptomics.

Granuloma annulare (GA) is an idiopathic condition characterized by granulomatous inflammation in the skin. Prior studies have suggested that GA develops from various triggers, leading to a complex interplay involving innate and adaptive immunity, tissue remodeling, and fibrosis. Macrophages are the major immune cells comprising GA granulomas, however, the molecular drivers and inflammatory signaling cascade behind macrophage activation is poorly understood. Histologically, GA exhibits both palisaded and interstitial patterns on histology, however the molecular composition of GA at the spatial level remains unexplored. GA is a condition without FDA-approved therapies despite the significant impact of GA on quality of life. Spatial transcriptomics is a valuable tool for profiling localized, genome-wide gene expression changes across tissue with emerging applications in clinical medicine. To improve our understanding of the spatially localized gene expression patterns underlying GA, we profiled the spatial gene expression landscape from six patients with GA. Our findings revealed mixed Th1 and Th2 signals comprising the GA microenvironment and spatially distinct M1 and M2 macrophage polarization characteristics. IFN-γ and TNF signals emerged as important regulators of GA granulomatous inflammation and interleukin-32 emerged as a key driver of granulomatous inflammation. Overall, our spatial transcriptomics data indicate that GA exhibits mixed immune and macrophage polarization.

A large-scale standardized physiological survey reveals functional organization of the mouse visual cortex.

To understand how the brain processes sensory information to guide behavior, we must know how stimulus representations are transformed throughout the visual cortex. Here we report an open, large-scale physiological survey of activity in the awake mouse visual cortex: the Allen Brain Observatory Visual Coding dataset. This publicly available dataset includes the cortical activity of nearly 60,000 neurons from six visual areas, four layers, and 12 transgenic mouse lines in a total of 243 adult mice, in response to a systematic set of visual stimuli. We classify neurons on the basis of joint reliabilities to multiple stimuli and validate this functional classification with models of visual responses. While most classes are characterized by responses to specific subsets of the stimuli, the largest class is not reliably responsive to any of the stimuli and becomes progressively larger in higher visual areas. These classes reveal a functional organization wherein putative dorsal areas show specialization for visual motion signals.